Sj"gren's Syndrome (SS) is autoinflammatory condition, characterized by a plethora of symptoms, including dry eyes and mouth. Despite the fact that the condition is relatively common, with up to 4 million persons affected in the US, accurate diagnosis has been complex and largely ineffective. Left undiagnosed, and thus untreated, SS can progress to significant morbidity and increase the risk of certain forms of cancer up to 100 fold. The identification of SS patients is, therefore, a pressing unmet need. Current SS diagnostic tools have been limited, in part because of the wide range of symptoms and frequent overlap with other autoinflammatory disorders. In this study, we will use genome-wide expression profiling of whole blood RNA coupled with multivariate analysis to identify SS diagnostic biomarkers of disease. Our preliminary data support the hypothesis that such biomarkers can be identified that correctly classify patients and provide a powerful adjunct to current SS diagnostic methods. Methods Our preliminary data demonstrate that a multivariate gene expression-based algorithm has superior performance to distinguish among SS, rheumatoid arthritis (RA), and unaffected controls than current diagnostic laboratory tests. In this proposal, a prototype multivariate algorithm will be developed utilizing systemic lupus erythematosus (SLE) as an additional condition within a standard SS differential. Moreover, we will only include RA and SLE patients without SS overlap to allow the potential for secondary SS diagnosis in future cohorts. PUBLIC HEALTH RELEVANCE: In this application we propose to create a simple blood test for the diagnosis of primary Sj"gren's Syndrome (pSS). Due to the fact that symptomologies can be subtle, varied and often masked by other auto inflammatory conditions, current technologies for diagnosis of this common condition have been ineffective. We propose to create a diagnostic test that takes these subtleties into account by including common autoimmune diseases that can occur simultaneously with pSS thereby, identifying pSS more effectively than currently possible.